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GETTING CONNECTED: SOCIAL SCIENCE IN THE AGE OF NETWORKS CAPSTONE PRESENTATION

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Title: An Algorithmic Approach to Computer Vision Author: Dan Huttenlocher Last modified by: dph Created Date: 4/8/1997 3:48:36 PM Document presentation format – PowerPoint PPT presentation

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Title: GETTING CONNECTED: SOCIAL SCIENCE IN THE AGE OF NETWORKS CAPSTONE PRESENTATION


1
GETTING CONNECTED SOCIAL SCIENCE IN THE AGE OF
NETWORKS CAPSTONE PRESENTATION
  • Presenters David Easley, Jon Kleinberg, Kathleen
    OConnor, Michael Macy, Dan Huttenlocher Rest of
    the Team John Abowd, Larry Blume, Geri Gay,
    Jeffrey Prince, David Strang Team Postdocs
    Mary Still, Ted Welser

April 23, 2008
2
The Cornell Networks Team
  • From across Cornell Arts Sciences, CALS, CIS,
    ILR, Johnson School

3
What are Networks?
Transportation Network
4
Social Networks with Data Collected by Hand
  • Nodes-people, Edges-friendships

Friendships in a 34-person karate club that split
apart---Zachary, 1977
5
Social Network Discovered from Traces of Online
Data
Email communication between 436 employees in HP
Research LabAdamic and Adar, 2005
6
Social Science and Networks
Trade flows between countries
Structure and Power
Blume, Easley, KleinbergTardos, 2007
KrempelPlumper, 2003
7
Cascades, the Spread of Rumors, the Reliability
of Information
Links between political blogs prior to 2004
election---AdamicGlance, 2005
8
Networks are Everywhere
  • The study of networks integrates ideas from the
    social sciences and computer science, as well as
    information science, statistics, biology,
    physics
  • The growth of the Internet has provided us with
    data that previously was difficult or impossible
    to obtain
  • Cornell is a leader in this area

9
Networks and the ISS
  • Encourage collaboration across disciplinary
    boundaries
  • Ongoing research between economists,
    sociologists, psychologists, and computer and
    information scientists
  • Engage the Cornell community (faculty, graduate
    students and undergrads) in cutting-edge research
  • Post docs
  • Graduate students
  • New undergrad courses with large enrollment

10
Theme Project Activities
  • Workshops, seminars, reading groups
  • Educational initiatives
  • Funding and recruiting opportunities
  • New inter-disciplinary research directions

11
Conferences
  • Ran conferences on aspects of project theme
  • Search and Diffusion in Social Networks
  • Symposium on Self-Organizing Online Communities
    (co-sponsored by Microsoft)
  • Brought national leaders from academia and
    industry to campus
  • E.g., Ron Burt, Nosh Contractor, Paul Dimaggio,
    Matt Jackson, Michael Kearns, Bob Kraut, Peter
    Monge, Duncan Watts, Barry Wellman

12
Educational Initiatives
  • New courses in all project areas, from
    introductory to graduate
  • Network material incorporated into existing
    courses
  • ECON, SOC, COMM, ILR, CIS, JGSM
  • Networks new intro undergrad course
  • Cross-listed in ECON, SOC, CS, INFO
  • This spring 280 students from 33 different majors

13
  • Networks
  • (ECON/SOC/CS/INFO 204)
  • A course on how the social, natural, and
    technological worlds are connected, and how the
    study of networks shed light on these
    connections. Topics include how opinions, fads,
    and political movements spread through society
    the robustness and fragility of food webs and
    financial markets and the technology, economics,
    and politics of Web information and on-line
    communities.

High-school dating (Bearman, Moody, Stovel 2004)
Corporate e-mail (Adamic and Adar, 2005)
14
Networks Class Blog
15
Recruiting and Funding
  • Networks activity on campus enhanced many other
    efforts
  • Recruiting directions related to networks in
    Sociology, Communication, and CIS
  • Large-Scale NSF funding
  • Cyberinfrastructure tools (2005-present)
  • New proposals being pursued by expanded version
    of project team

16
New Research Directions
  • Networks activity drew in many faculty beyond
    original project team
  • New research informed by perspectives from
    multiple areas
  • Next two examples (out of many)
  • Social cognition and individual behavior
  • Social contagion and on-line communities

17
Social Networks Represent Relationships Among
People
  • People work collaboratively, share opinions,
    create new knowledge through their decisions to
    build a relationship (or not)

18
Micro-Foundations of Social Networks
  • Systematic investigations into factors that
    influence peoples
  • Cognitions about their social networks
  • Intentions to create relationships (ties)
  • Efforts to create relationships
  • Goals
  • Understanding how networks evolve
  • A psychological account of the spread of
    influence and ideas in social systems

19
People and their Network Positions
  • Personality psychology perspective
  • People are endowed with traits that are
    heritable, unaffected by external influences, and
    stable across the life span
  • Links between peoples traits and their positions
    in their social networks (Klein, Lim, Saltz,
    Mayer, 2004)
  • People who are high in neuroticism tend to be
    less central in their networks (advice and
    friendship)

20
A Novel Social Network on Second Life
James (beard)
Mark (UK)
Jill (pink)
Ben (glasses)
Mary (brown pants)
Emma (penguin)
Scene from Second Life
21
Where We Are Going
  • How do people understand and navigate their
    social environments to gain resources they care
    about?
  • Develop interventions to teach people strategies
    to make them more effective
  • Better able to spot opportunities to build social
    capital
  • Better able to translate those opportunities into
    advantageous network positions
  • New forms of social engagement and interaction
    give us new (and improved?) ways of studying
    social cognition and social behavior

22
It certainly is a small world!
Thats amazing you know my Uncle Charlie!
A Chance Encounter in a Distant Land Leads to
Small Talk
23
Six Degrees of Separation
  • The planet is very large 6.5b!

Yet the world is small 6
How is this possible?
24
Adding to the Mystery
  • Easy to explain if the social ties were random
  • But friendships tend to be highly clustered

B
A
C
25
(No Transcript)
26
Solved by Watts Strogatz
  • A few long-range ties
  • Create shortcuts between otherwise distant nodes
  • While preserving the clustering of a social
    network

27
The Strength of Weak Ties
  • Long-range ties tend to be relationally weak
  • Less frequent interaction
  • Lower trust and influence
  • But structurally strong
  • Access to new ideas and information
  • Accelerate the spread of disease

28
Weak Ties Are Key
  • Whatever is to be diffused can reach a larger
    number of people, and traverse a greater social
    distance, when passed through weak ties rather
    than strong.
  • -- Mark Granovetter, 1973
  • A truism across the social information sciences
  • But there are some intriguing anomalies...

29
The Chain-Letter Paradox
  • If most people are separated by only six
    degrees, why are chain letters hundreds of links
    long?

Sequence of signatures on e-mail chain letter
protesting the Iraq war, with 18,119 nodes,
median depth is 288.
Liben-Nowell Kleinberg 2008, Tracing
information flow on a global scale using Internet
chain-letter data, PNAS 1054633-38.
30
The Problem of Critical Mass
  • If an epidemic can quickly leap continents and
    reach millions of people in a few days, why do
    social movements often spread spatially and
    incrementally prior to reaching a take-off
    point?

31
Why Are Communities Clustered?
  • A cluster is a dense cloud of mutual friends
  • How do these form?
  • Conventional wisdom people join communities and
    then become mutual friends
  • Maybe it is actually the other way around people
    join communities to be with mutual friends?

32
Social Cloud Formation
  • 875 LiveJournal (blogging) communities
  • Individuals one degree removed
  • Joining as a function of
  • Number of friends who are already members
  • Clustering among friends

Backstrom, Huttenlocher, Kleinberg, Lan, 2006.
Group Formation in Large Social Networks
Membership, Growth, Evolution, Proc. 12th ACM
SIGKDD Intl. Conf. on Knowledge Discovery Data
Mining.
33
Number and clustering of friends
A B C
Time 1
34
Number and clustering of friends
A B C
Time 2
35
Number and clustering of friends
A B C
Time 3
36
Number and clustering of friends
A B C
Time 4
37
Number and clustering of friends
A B C
Time 5
38
Number and clustering of friends
A B C
Time 6
39
Number and clustering of friends
A B C
Time 7
40
Number and clustering of friends
A B C
Time 8
41
Why is Clustering Important?
  • Chain-letters and social movements seem to avoid
    taking shortcuts
  • Its the mutual friends that seem to be key to
    growth of communities
  • If disease and information can take shortcuts,
    why cant social contagions?

42
A Simple Explanation
  • Social contagions differ from disease and
    information
  • Acquiring information is not the same thing as
    acting on it
  • The same information from two friends is
    redundant
  • The same advice from two friends is not
  • Credibility, legitimacy utility of adoption
    usually increase with the number of prior adopters
  • Centola, D. and M. Macy. 2007. Complex
    Contagions the Weakness of Long Ties. American
    Journal of Sociology 113702-34

43
Maybe Its Not Such a Small World After All?
  • Information and disease benefit from weak ties
    that create shortcuts
  • A single contact is sufficient for transmission
  • Clustering is therefore redundant
  • Social contagions benefit from clustering
  • Redundancy provides social reinforcement
  • Long-range ties inform but do not persuade

44
1000000
100000
Timesteps
100000
Simple contagion that requires adoption by 1
neighbor
10000
0 .1 .2 .3 .4
.5 .6 .7 .8 .9
1
(High Clustering)
(No Clustering)
Proportion of Random Ties
Random ties promote the spread of information
(lower is faster)
45
10000000
Social contagion that requires adoption by 3
neighbors
Phase transition in the social fabric Contagion
can no longer spread
Social contagion that requires adoption by 2
neighbors
1000000
Timesteps
100000
Simple contagion that requires adoption by 1
neighbor
10000
0 .1 .2 .3 .4
.5 .6 .7 .8 .9
1
(High Clustering)
(No Clustering)
Proportion of Random Ties
But not the spread of social contagions
46
Small Worlds in a Bigger Picture
  • Social life is hard to observe
  • You can interview friends, but you cannot
    interview a friendship
  • Fleeting interaction
  • In private
  • Tedious to record over time, especially in large
    groups

47
Why This is Changing
  • Humans increasingly interact publicly online
  • Web pages, Facebook, blogs, wikis, games
  • Computer-mediated interaction leaves digital
    traces
  • New era of connectionist social science?
  • Interactions among people, not just variables
  • Networks, not just aggregates of individuals
  • Dynamics, not just comparative statics
  • Links the talents tools of social, computer,
    and information scientists

48
  • Some closing observations
  • Whats next

49
Observations
  • What does it mean to do interdisciplinary work
    with a dozen faculty across such broad range of
    fields?
  • Sociology, economics, communications, social
    psychology, information science, computer science
  • More than joint projects across disciplinary
    boundaries catalyst for research
  • Investigations deeply informed and motivated by
    research of members in other fields but
    published in established (disciplinary) venues

50
Observations
  • Importance of residential year, with lead-in and
    follow-up years
  • Build deeper ties and understanding across
    disciplines through seminars, visitors,
    workshops, proposals, informal discussion
  • Exposure to both classical literature and current
    work in several areas
  • Educational initiatives at both graduate and
    undergraduate level also engage team members in
    broader understanding
  • Research that happened as a result

51
Observations
  • Qualitative change in external visibility of
    Cornell in networks area
  • In both social sciences and computer science
  • Had good basis for this in prior activities by
    various individuals both on team and others
  • Institutional commitment and increased activity
    level both important for the boost
  • Holding interdisciplinary workshops with the best
    people in the world they leave impressed with
    Cornell

52
Whats Next
  • The team, plus a number of others, is planning to
    continue working together
  • The Information Science program provides a
    natural inter-disciplinary venue for continued
    interaction
  • We are seeking large-scale external funding for
    this research
  • NSF CISE Expeditions proposal would be 5 years at
    2M/yr
  • Will pursue that program and others at similar
    scale

53
Whats Next
  • Build on the increased visibility and momentum in
    research activity
  • Long-term institutional impact
  • Best way we see to do this is coordinated faculty
    hiring in networks area
  • Joint appointments, or joint recruiting
    committees for single department hires

54
  • We want to give our thanks to the ISS for
    supporting this project!
  • Thanks also to Microsoft for additional support
    of postdocs and workshops
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